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tmux cheat sheet

(C-x means ctrl+x, M-x means alt+x)

Prefix key

The default prefix is C-b. If you (or your muscle memory) prefer C-a, you need to add this to ~/.tmux.conf:

remap prefix to Control + a

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import random
import pandas
from sklearn.cross_validation import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.preprocessing import LabelEncoder
from sklearn.utils import check_array

import tensorflow as tf
from tensorflow.contrib import layers

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import dbm, os
import cPickle as pickle
from gensim.models import Word2Vec
import numpy as np

def save_model(model, directory):
    model.init_sims() # making sure syn0norm is initialised
    if not os.path.exists(directory):
        os.makedirs(directory)
    # Saving indexes as DBM'ed dictionary

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#!/usr/bin/env python

def strip_output(nb):
    for cell in nb.cells:
        if hasattr(cell, "outputs"):
            cell.outputs = []
        if hasattr(cell, "prompt_number"):
            del cell["prompt_number"]

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##VGG16 model for Keras

This is the Keras model of the 16-layer network used by the VGG team in the ILSVRC-2014 competition.

It has been obtained by directly converting the Caffe model provived by the authors.

Details about the network architecture can be found in the following arXiv paper:

Very Deep Convolutional Networks for Large-Scale Image Recognition

K. Simonyan, A. Zisserman

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A Deep Siamese Network for Scene Detection

This is a model from the paper:

A Deep Siamese Network for Scene Detection in Broadcast Videos
Lorenzo Baraldi, Costantino Grana, Rita Cucchiara
Proceedings of the 23rd ACM International Conference on Multimedia, 2015

Please cite the paper if you use the models.

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/build/
*.so
*.cpp
/cachegrind.out.profilestats
/profilestats.prof
/build1darray.s

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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os.path
import re
import sys
import tarfile

import numpy as np

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# Working example for my blog post at:
# https://danijar.github.io/structuring-your-tensorflow-models
import functools
import tensorflow as tf
import sets


def lazy_property(function):
    attribute = '_' + function.__name__

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import pandas as pd
from io import StringIO

datastring = StringIO("""\
ticker  avg_spread  max_spread  timestamp
a       0.22        1.84        2016-06-03 03:00:00
aa      0.01        0.10        2016-06-03 02:00:00
aaap    2.07        2.17        2016-06-03 01:00:00
aal     0.15        0.5         2016-06-03 04:00:00
""")